Selected ‘Starter Kit’ energy system modelling data for Ecuador (#CCG)

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Ecuador, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work. Table   Speci cations Table   Subject Energy

Speci cations
Speci c subject area Energy System Modelling Type of data Tables  Graphs  Charts  Description of modelling assumptions How data were acquired Literature survey (databases and reports from international organisations; journal articles)

Data format Raw and Analysed
Parameters for data collection Data collected based on inputs required to create an energy system model for Ecuador Description of data collection Data were collected from the websites, annual reports and databases of international organisations, as well as from academic articles and existing modelling databases.

Value Of The Data
These data can be used to develop national energy system models to inform national energy investment outlooks and policy plans, as well as provide insights on the evolution of the electricity supply system under different trajectories.
The data are useful for country analysts, policy makers and the broader scienti c community, as a zero-order starting point for model development.
These data could be used to examine a range of possible energy system pathways, in addition to the examples given in this study, to provide further insights on the evolution of the country's power system.
The data can be used both for conducting an analysis of the power system but also for capacity building activities. Also, the methodology of translating the input data into modelling assumptions for a cost-optimization tool is presented here which is useful for developing a zero order Tier 2 national energy model [1]. This is consistent with U4RIA energy planning goals [2].

Data Description
The data provided in this paper can be used as input data to develop an energy system model for Ecuador. As an illustration, these data were used to develop an energy system model using the cost-optimization tool OSeMOSYS for the period 2015-2050. For reference, that model is described in Appendix A and its data les are available as Supplementary Materials. Appendix gure A3 for Ecuador is repeated below. This is purely illustrative. It shows a zero-order model of the production of electricity by technology over the period 2020 to 2050 for a least cost energy future. Using the data described in this article, the analyst can reproduce this, as well as many other scenarios, such as net-zero by 2050, in a variety of energy planning toolkits.
The data provided were collected from publicly available sources, including the reports of international organizations, journal articles and existing model databases. The dataset includes the techno-economic parameters of supply-side technologies, installed capacities, emissions factors and nal electricity demands. Below shows the different items and their description, in order of appearance, presented in this article.

Item
Description of Content

Existing Electricity Supply System
The total power generation capacity in Ecuador is estimated at 6733.86 MW in 2018 [3,4,5,6]. The estimated existing power generation capacity is detailed in Table 1 below [3,4,5,6]. The methods used to calculate these estimates are described in more detail in Section 2.1.

Techno-economic Data for Electricity Generation Technologies
The techno-economic parameters of electricity generation technologies are presented in Table 2, including costs, operational lives, e ciencies and average capacity factors. Cost (capital and xed), operational life and e ciency data are based on the data used in the South America Model Base [7] and are applicable to South America. Projected cost reductions for renewable energy technologies were estimated by applying the cost reduction trends from a 2021 IRENA report focussing on Africa [8] to these South America-speci c current cost estimates. These projections are presented in Table 3 [3,10,11], as well as an NREL dataset [12]. Capacity factors for other technologies were sourced from SAMBA [7] and are applicable to South America. Average capacity factors were calculated for each technology and presented in the table below, with daytime (6am -6pm) averages presented for solar PV technologies. For more information on the capacity factor data, refer to Section 2.1.

Techno-economic Data for Power Transmission and Distribution
The e ciency of power transmission and distribution were taken from the SAMBA dataset [7], which gives estimated e ciencies by country, including

Techno-economic Data for Re neries
Ecuador has an estimated 176kb/d domestic re nery capacity [13]. In the OSeMOSYS model, two oil re nery technologies were made available for investment in the future, each with different output activity ratios for Heavy Fuel Oil (HFO) and Light Fuel Oil (LFO). The technoeconomic data for these technologies are shown in Table 5.

Fuel Prices
Assumed costs are provided for both imported and domestically-extracted fuels. The fuel price projections until 2050 are presented below. These are estimates based on an international oil price forecast [15] for oil and oil products, the SAMBA dataset [7] for natural gas, and a report on international biomass markets [16]. More detail is provided in Section 2.2.

Emission Factors
Fossil fuel technologies emit several greenhouse gases, including carbon dioxide, methane and nitrous oxides throughout their operational lifetime. In this analysis, only carbon dioxide emissions are considered. These are accounted for using carbon dioxide emission factors assigned to each fuel, rather than each power generation technology. The assumed emission factors are presented in Table 7.  Tables 8 and 9 show estimated domestic renewable energy potentials and fossil fuel reserves respectively in Ecuador.  Natural Gas (trillion cubic feet) 0.0

Electricity Demand Projection
An electricity demand projection was calculated based on the Current Policy Scenario regional demand projections of the OLADE Energy Outlook 2019 [22], which were divided by country based on historic consumption data from the International Energy Agency (IEA) [23].  [7]. The data sources used are detailed in this section.

Electricity Supply System Data
Data on Ecuador's existing on-grid power generation capacity, presented in Table 1, were extracted from the PLEXOS World dataset [3,4,5] using scripts from OSeMOSYS global model generator [24]. PLEXOS World provides estimated capacities and commissioning dates by power plant, based on the World Resources Institute Global Power Plant database [5].These data were used to estimate installed capacity in future years based on the operational life data in Table 2. Data on Ecuador's off-grid renewable energy capacity were sourced from yearly capacity statistics produced by IRENA [6]. Cost, e ciency and operational life data in Table 2 were primarily collected from the SAMBA dataset [7], which provides estimates for these parameters by technology in South America. Where estimates were not available in SAMBA, costs were extrapolated from reports by IRENA for diesel electricity generation, medium hydropower, and off-grid solar PV [8,9]. The costs of renewable energy technologies are expected to fall in the future. In order to calculate estimated cost reductions in the region, technology-speci c cost reduction trends from a very recent IRENA report focussing on Africa [8] were applied to the regional current cost estimates used from SAMBA [7,8,9]. For offshore wind, the cost reduction trend was instead taken from a technology-speci c IRENA report on the future of wind [25] since it is not featured in [8]. The resulting cost projections are presented in Table 3 and Figure 2. It is assumed that costs fall linearly between data points and those costs remain constant beyond 2040 when the IRENA forecasts end (except for offshore wind, where the IRENA forecast continues to 2050 Country-speci c capacity factors for solar PV, onshore wind and hydropower were sourced from Renewables Ninja and the PLEXOS-World 2015 Model Dataset [3,10,11]. These sources provide hourly capacity factors for 2015 for solar PV and wind, and 15-year average monthly capacity factors for hydropower, the average values of which are presented in Table 2. Country-speci c capacity factors for offshore wind were estimated based on an NREL source that gives estimates of the potential wind power capacity by capacity factor range in each country [12], from which a capacity-weighted average was calculated. The capacity factor data were also used to estimate capacity factors for 8 time slices used in the OSeMOSYS model (see detail in Annex 1).
Capacity factors for other technologies were sourced from SAMBA [7], which provides estimated capacity factors for South America. The capital costs, operational lives, and e ciencies of power transmission and distribution were also taken from SAMBA [7], which provides future projections. Technoeconomic data for re neries were sourced from the IEA Energy Technology Systems Analysis Programme (ETSAP) [14], which provides generic estimates of costs and performance parameters, while the re nery options modelled are based on the methods used in The Electricity Model Base for Africa (TEMBA) [26].

Fuel Data
Fuel price projections for crude oil were taken from a 2020 US EIA oil price forecast [15], based on which projections for LFO and HFO were estimated by increasing the price by 1/3 for LFO and reducing the price by 20% for HFO, as done in TEMBA [26]. The natural gas price forecast was taken from SAMBA, which provides country-speci c forecasts to 2063 [7]. The domestic biomass price was estimated based on a report on international biomass markets [16] that includes cost estimates for biomass production in Brazil. This cost was increased by 10% to estimate a price for imported biomass, re ecting the cost of importation.

Emissions Factors and Domestic Reserves
Emissions factors were collected from the IPCC Emission Factor Database [17], which provides carbon emissions factors by fuel. The domestic solar and wind resources were collected from NREL datasets, which provide estimates of potential yearly generation by country [12,18]. Other renewable energy potentials were sourced from a regional report by OLADE [19] and the World Small Hydropower Development Report [20], which provide estimated potentials by country. The large and medium hydropower potential was estimated by subtracting the small hydropower potential [20] from the estimated overall hydropower potential [19]. Estimated domestic coal and oil reserves were sourced from the SAMBA dataset [17], while natural gas reserves were sourced from the 2019 BP Statistical Review [21], which provide estimates of reserves by country.

Electricity Demand Data
The nal electricity demand projection for Ecuador is based on the Current Policy Scenario of the OLADE Energy Outlook 2019 [22], which provides regional aggregated demand projections to 2040.These regional cost projections were divided by country using historical consumption data from the IEA [23], and extended to 2050 by extrapolating the growth trend to 2050.  Final Electricity Demand Projection (PJ) [22,23] Supplementary Files This is a list of supplementary les associated with this preprint. Click to download. EcuadorLCv2.txt EcuadorFF.txt EcuadorNZv2.txt appendix.docx